class_docstr: Random search oracle.
  
  Args:
      objective: A string, `keras_tuner.Objective` instance, or a list of
          `keras_tuner.Objective`s and strings. If a string, the direction of
          the optimization (min or max) will be inferred. If a list of
          `keras_tuner.Objective`, we will minimize the sum of all the
          objectives to minimize subtracting the sum of all the objectives to
          maximize. The `objective` argument is optional when
          `Tuner.run_trial()` or `HyperModel.fit()` returns a single float as
          the objective to minimize.
      max_trials: Integer, the total number of trials (model configurations)
          to test at most. Note that the oracle may interrupt the search
          before `max_trial` models have been tested if the search space has
          been exhausted. Defaults to 10.
      seed: Optional integer, the random seed.
      hyperparameters: Optional `HyperParameters` instance. Can be used to
          override (or register in advance) hyperparameters in the search
          space.
      tune_new_entries: Boolean, whether hyperparameter entries that are
          requested by the hypermodel but that were not specified in
          `hyperparameters` should be added to the search space, or not. If
          not, then the default value for these parameters will be used.
          Defaults to True.
      allow_new_entries: Boolean, whether the hypermodel is allowed to
          request hyperparameter entries not listed in `hyperparameters`.
          Defaults to True.
      max_retries_per_trial: Integer. Defaults to 0. The maximum number of
          times to retry a `Trial` if the trial crashed or the results are
          invalid.
      max_consecutive_failed_trials: Integer. Defaults to 3. The maximum
          number of consecutive failed `Trial`s. When this number is reached,
          the search will be stopped. A `Trial` is marked as failed when none
          of the retries succeeded.
docstr: Fill the hyperparameter space with values.
  
  Args:
      trial_id: A string, the ID for this Trial.
  
  Returns:
      A dictionary with keys "values" and "status", where "values" is
      a mapping of parameter names to suggested values, and "status"
      should be one of "RUNNING" (the trial can start normally), "IDLE"
      (the oracle is waiting on something and cannot create a trial), or
      "STOPPED" (the oracle has finished searching and no new trial should
      be created).
docstr_tok: ["Fill","the","hyperparameter","space","with","values",".","Args",":","trial_id",":","A","string",",","the","ID","for","this","Trial",".","Returns",":","A","dictionary","with","keys","``","values","''","and","``","status","''",",","where","``","values","''","is","a","mapping","of","parameter","names","to","suggested","values",",","and","``","status","''","should","be","one","of","``","RUNNING","''","(","the","trial","can","start","normally",")",",","``","IDLE","''","(","the","oracle","is","waiting","on","something","and","can","not","create","a","trial",")",",","or","``","STOPPED","''","(","the","oracle","has","finished","searching","and","no","new","trial","should","be","created",")","."]
code: def populate_space(self, trial_id):
      """Fill the hyperparameter space with values.
  
      Args:
          trial_id: A string, the ID for this Trial.
  
      Returns:
          A dictionary with keys "values" and "status", where "values" is
          a mapping of parameter names to suggested values, and "status"
          should be one of "RUNNING" (the trial can start normally), "IDLE"
          (the oracle is waiting on something and cannot create a trial), or
          "STOPPED" (the oracle has finished searching and no new trial should
          be created).
      """
      values = self._random_values()
      if values is None:
          return {
              "status": trial_module.TrialStatus.STOPPED,
              "values": None,
          }
      return {
          "status": trial_module.TrialStatus.RUNNING,
          "values": values,
      }
  
file_path: keras-tuner/keras_tuner/tuners/randomsearch.py
imports: from keras_tuner.api_export import keras_tuner_export
  from keras_tuner.engine import oracle
  from keras_tuner.engine import trial
  from keras_tuner.engine import tuner
label_desc: Use image node_id 2 for calling the RandomSearchOracle obj's underlying member method code with example usage: obj.populate_space(trial_id) and returns: dict, dict